Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations28327324
Missing cells7885065
Missing cells (%)1.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.7 GiB
Average record size in memory365.8 B

Variable types

Categorical5
Numeric17

Alerts

id_categoria has constant value "1"Constant
canal is highly overall correlated with segmentoHigh correlation
densidad_hab is highly overall correlated with superficie_km2High correlation
dolar is highly overall correlated with id_periodo and 2 other fieldsHigh correlation
id_barrio is highly overall correlated with id_comunaHigh correlation
id_comuna is highly overall correlated with id_barrioHigh correlation
id_periodo is highly overall correlated with dolar and 2 other fieldsHigh correlation
segmento is highly overall correlated with canalHigh correlation
superficie_km2 is highly overall correlated with densidad_habHigh correlation
tpm is highly overall correlated with dolar and 2 other fieldsHigh correlation
uf is highly overall correlated with dolar and 2 other fieldsHigh correlation
segmento is highly imbalanced (55.3%)Imbalance
descr_flag_patente is highly imbalanced (91.5%)Imbalance
indice_gse has 1645935 (5.8%) missing valuesMissing
n_habitantes has 1645935 (5.8%) missing valuesMissing
n_ptos_interes has 476688 (1.7%) missing valuesMissing
superficie_km2 has 476688 (1.7%) missing valuesMissing
densidad_hab has 1645935 (5.8%) missing valuesMissing
tasa_desempleo has 601516 (2.1%) missing valuesMissing
liq_um is highly skewed (γ1 = 78.47871768)Skewed
liq_um has 433340 (1.5%) zerosZeros
ipc has 1684246 (5.9%) zerosZeros
imacec has 1098185 (3.9%) zerosZeros

Reproduction

Analysis started2025-10-18 17:38:47.488678
Analysis finished2025-10-18 18:09:06.527328
Duration30 minutes and 19.04 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

id_categoria
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 GiB
1
28327324 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28327324
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
128327324
100.0%

Length

2025-10-18T15:09:06.550888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-18T15:09:06.576273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
128327324
100.0%

Most occurring characters

ValueCountFrequency (%)
128327324
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)28327324
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
128327324
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)28327324
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
128327324
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)28327324
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
128327324
100.0%

id_cliente
Real number (ℝ)

Distinct97065
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean372401.02
Minimum13
Maximum727517
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size216.1 MiB
2025-10-18T15:09:06.612069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile60522
Q1190023
median374107
Q3556663
95-th percentile689253
Maximum727517
Range727504
Interquartile range (IQR)366640

Descriptive statistics

Standard deviation203824.56
Coefficient of variation (CV)0.54732546
Kurtosis-1.2000749
Mean372401.02
Median Absolute Deviation (MAD)182834
Skewness-0.072202653
Sum1.0549124 × 1013
Variance4.1544451 × 1010
MonotonicityNot monotonic
2025-10-18T15:09:06.668247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3103235070
 
< 0.1%
5637424794
 
< 0.1%
3174194738
 
< 0.1%
3869094616
 
< 0.1%
7033854563
 
< 0.1%
3975904541
 
< 0.1%
1196664512
 
< 0.1%
4350324479
 
< 0.1%
2457534426
 
< 0.1%
2296244414
 
< 0.1%
Other values (97055)28281171
99.8%
ValueCountFrequency (%)
1315
 
< 0.1%
33290
 
< 0.1%
511549
< 0.1%
56452
 
< 0.1%
62126
 
< 0.1%
643
 
< 0.1%
86278
 
< 0.1%
122454
 
< 0.1%
14865
 
< 0.1%
1494
 
< 0.1%
ValueCountFrequency (%)
7275171
 
< 0.1%
7275161
 
< 0.1%
7275151
 
< 0.1%
727513179
< 0.1%
727512352
< 0.1%
7275101
 
< 0.1%
727507159
< 0.1%
727505158
< 0.1%
7274949
 
< 0.1%
72749317
 
< 0.1%

id_periodo
Real number (ℝ)

High correlation 

Distinct84
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202190.02
Minimum201809
Maximum202508
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size216.1 MiB
2025-10-18T15:09:06.721480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum201809
5-th percentile201902
Q1202010
median202206
Q3202401
95-th percentile202504
Maximum202508
Range699
Interquartile range (IQR)391

Descriptive statistics

Standard deviation199.64598
Coefficient of variation (CV)0.00098741759
Kurtosis-1.0544741
Mean202190.02
Median Absolute Deviation (MAD)195
Skewness-0.11911251
Sum5.7275023 × 1012
Variance39858.519
MonotonicityNot monotonic
2025-10-18T15:09:06.779620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202212431300
 
1.5%
202312410059
 
1.4%
202412407005
 
1.4%
202303404418
 
1.4%
202112394008
 
1.4%
202211391564
 
1.4%
202109389822
 
1.4%
202502388200
 
1.4%
202401387052
 
1.4%
202203386607
 
1.4%
Other values (74)24337289
85.9%
ValueCountFrequency (%)
201809260451
0.9%
201810252172
0.9%
201811283500
1.0%
201812314043
1.1%
201901298732
1.1%
201902298273
1.1%
201903299924
1.1%
201904280120
1.0%
201905278713
1.0%
201906252741
0.9%
ValueCountFrequency (%)
202508325489
1.1%
202507333226
1.2%
202506308601
1.1%
202505337662
1.2%
202504341065
1.2%
202503385217
1.4%
202502388200
1.4%
202501385482
1.4%
202412407005
1.4%
202411364850
1.3%

tipo_mix
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 GiB
PREMIUM
15605459 
MASIVO
12721865 

Length

Max length7
Median length7
Mean length6.5508977
Min length6

Characters and Unicode

Total characters185569403
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPREMIUM
2nd rowPREMIUM
3rd rowPREMIUM
4th rowPREMIUM
5th rowPREMIUM

Common Values

ValueCountFrequency (%)
PREMIUM15605459
55.1%
MASIVO12721865
44.9%

Length

2025-10-18T15:09:06.829793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-18T15:09:06.861055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
premium15605459
55.1%
masivo12721865
44.9%

Most occurring characters

ValueCountFrequency (%)
M43932783
23.7%
I28327324
15.3%
R15605459
 
8.4%
P15605459
 
8.4%
E15605459
 
8.4%
U15605459
 
8.4%
A12721865
 
6.9%
S12721865
 
6.9%
V12721865
 
6.9%
O12721865
 
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)185569403
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M43932783
23.7%
I28327324
15.3%
R15605459
 
8.4%
P15605459
 
8.4%
E15605459
 
8.4%
U15605459
 
8.4%
A12721865
 
6.9%
S12721865
 
6.9%
V12721865
 
6.9%
O12721865
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)185569403
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M43932783
23.7%
I28327324
15.3%
R15605459
 
8.4%
P15605459
 
8.4%
E15605459
 
8.4%
U15605459
 
8.4%
A12721865
 
6.9%
S12721865
 
6.9%
V12721865
 
6.9%
O12721865
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)185569403
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M43932783
23.7%
I28327324
15.3%
R15605459
 
8.4%
P15605459
 
8.4%
E15605459
 
8.4%
U15605459
 
8.4%
A12721865
 
6.9%
S12721865
 
6.9%
V12721865
 
6.9%
O12721865
 
6.9%

id_sku_venta
Real number (ℝ)

Distinct520
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean428579.43
Minimum515
Maximum604926
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size216.1 MiB
2025-10-18T15:09:06.899663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum515
5-th percentile7493
Q1450325
median450604
Q3450749
95-th percentile451269
Maximum604926
Range604411
Interquartile range (IQR)424

Descriptive statistics

Standard deviation104985.77
Coefficient of variation (CV)0.24496222
Kurtosis12.112201
Mean428579.43
Median Absolute Deviation (MAD)201
Skewness-3.5935882
Sum1.2140508 × 1013
Variance1.1022012 × 1010
MonotonicityNot monotonic
2025-10-18T15:09:06.953350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4506071159881
 
4.1%
450604981474
 
3.5%
450592918378
 
3.2%
450684914142
 
3.2%
450237907067
 
3.2%
450133852698
 
3.0%
450403749381
 
2.6%
592741858
 
2.6%
450746728508
 
2.6%
450417721352
 
2.5%
Other values (510)19652585
69.4%
ValueCountFrequency (%)
51565937
 
0.2%
566369
 
< 0.1%
592741858
2.6%
595130305
 
0.5%
622116009
 
0.4%
76323991
 
0.1%
76580408
 
0.3%
748114234
 
0.1%
748212103
 
< 0.1%
748359447
 
0.2%
ValueCountFrequency (%)
6049263169
 
< 0.1%
6045832105
 
< 0.1%
60458234283
 
0.1%
604581390476
1.4%
60458042379
 
0.1%
60453345
 
< 0.1%
604514494
 
< 0.1%
60450932340
 
0.1%
6045085513
 
< 0.1%
4515007287
 
< 0.1%

liq_um
Real number (ℝ)

Skewed  Zeros 

Distinct16318
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.93081289
Minimum-89.46
Maximum3096.6533
Zeros433340
Zeros (%)1.5%
Negative4395
Negative (%)< 0.1%
Memory size216.1 MiB
2025-10-18T15:09:07.009225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-89.46
5-th percentile0.0462
Q10.084
median0.21
Q30.588
95-th percentile2.94
Maximum3096.6533
Range3186.1133
Interquartile range (IQR)0.504

Descriptive statistics

Standard deviation7.3722788
Coefficient of variation (CV)7.9202586
Kurtosis12213.228
Mean0.93081289
Median Absolute Deviation (MAD)0.1512
Skewness78.478718
Sum26367438
Variance54.350495
MonotonicityNot monotonic
2025-10-18T15:09:07.064852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.078961776867
 
6.3%
0.055441702889
 
6.0%
0.0841338104
 
4.7%
0.157921214046
 
4.3%
0.0588912625
 
3.2%
0.168902608
 
3.2%
0.11088780586
 
2.8%
0.05964754101
 
2.7%
0.23688752916
 
2.7%
0.042651926
 
2.3%
Other values (16308)17540656
61.9%
ValueCountFrequency (%)
-89.461
< 0.1%
-63.1681
< 0.1%
-56.85121
< 0.1%
-52.982161
< 0.1%
-47.77081
< 0.1%
-38.348521
< 0.1%
-35.7841
< 0.1%
-33.39841
< 0.1%
-33.2641
< 0.1%
-31.5841
< 0.1%
ValueCountFrequency (%)
3096.653281
< 0.1%
2949.94561
< 0.1%
2527.667521
< 0.1%
2349.84961
< 0.1%
2336.979121
< 0.1%
2128.76161
< 0.1%
2021.3761
< 0.1%
1958.2081
< 0.1%
1953.47041
< 0.1%
1895.041
< 0.1%

id_barrio
Real number (ℝ)

High correlation 

Distinct1981
Distinct (%)< 0.1%
Missing90113
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean916951.61
Minimum110110
Maximum1510511
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size216.1 MiB
2025-10-18T15:09:07.120947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum110110
5-th percentile310113
Q1610815
median910117
Q31311212
95-th percentile1360310
Maximum1510511
Range1400401
Interquartile range (IQR)700397

Descriptive statistics

Standard deviation365671.18
Coefficient of variation (CV)0.39879006
Kurtosis-1.0680288
Mean916951.61
Median Absolute Deviation (MAD)399306
Skewness-0.24201081
Sum2.5892156 × 1013
Variance1.3371541 × 1011
MonotonicityNot monotonic
2025-10-18T15:09:07.177746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1020110133284
 
0.5%
1010121131312
 
0.5%
1312419123953
 
0.4%
840124110610
 
0.4%
1030123109482
 
0.4%
620110108945
 
0.4%
510310108122
 
0.4%
810213101642
 
0.4%
131012493454
 
0.3%
101091583774
 
0.3%
Other values (1971)27132633
95.8%
(Missing)90113
 
0.3%
ValueCountFrequency (%)
11011039877
0.1%
11011111568
 
< 0.1%
11011231914
0.1%
11011330080
0.1%
11011431371
0.1%
11011521677
0.1%
11011636604
0.1%
11011728046
0.1%
11011847995
0.2%
1101195516
 
< 0.1%
ValueCountFrequency (%)
151051121528
0.1%
151012852
 
< 0.1%
151012614548
0.1%
15101254743
 
< 0.1%
151012425282
0.1%
151012317476
0.1%
151012232535
0.1%
15101216205
 
< 0.1%
151012016048
0.1%
151011912902
 
< 0.1%

id_comuna
Real number (ℝ)

High correlation 

Distinct326
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9173.6821
Minimum1101
Maximum15105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size216.1 MiB
2025-10-18T15:09:07.228872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1101
5-th percentile3101
Q16109
median9101
Q313112
95-th percentile13603
Maximum15105
Range14004
Interquartile range (IQR)7003

Descriptive statistics

Standard deviation3658.0398
Coefficient of variation (CV)0.39875371
Kurtosis-1.0664497
Mean9173.6821
Median Absolute Deviation (MAD)3993
Skewness-0.24539343
Sum2.5986586 × 1011
Variance13381255
MonotonicityNot monotonic
2025-10-18T15:09:07.284448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13101710538
 
2.5%
5101541275
 
1.9%
5109533766
 
1.9%
10101504136
 
1.8%
2101496689
 
1.8%
9101478695
 
1.7%
8101464934
 
1.6%
13110455889
 
1.6%
8401452928
 
1.6%
13119425316
 
1.5%
Other values (316)23263158
82.1%
ValueCountFrequency (%)
1101316881
1.1%
1107128403
 
0.5%
140121413
 
0.1%
2101496689
1.8%
210227953
 
0.1%
21037622
 
< 0.1%
210435320
 
0.1%
2201226946
0.8%
220343534
 
0.2%
230164522
 
0.2%
ValueCountFrequency (%)
1510521528
 
0.1%
15101303217
1.1%
1420475117
 
0.3%
1420332711
 
0.1%
1420244882
 
0.2%
1420178284
 
0.3%
1410884544
 
0.3%
1410738350
 
0.1%
1410668355
 
0.2%
1410436824
 
0.1%

segmento
Categorical

High correlation  Imbalance 

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 GiB
AL
11815483 
BO
11295651 
RT
1950447 
MA
1504001 
FS
 
1017052
Other values (14)
 
744690

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters56654648
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBO
2nd rowBO
3rd rowBO
4th rowBO
5th rowBO

Common Values

ValueCountFrequency (%)
AL11815483
41.7%
BO11295651
39.9%
RT1950447
 
6.9%
MA1504001
 
5.3%
FS1017052
 
3.6%
BA486982
 
1.7%
IE135488
 
0.5%
AP53830
 
0.2%
DI33053
 
0.1%
FF31506
 
0.1%
Other values (9)3831
 
< 0.1%

Length

2025-10-18T15:09:07.334231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
al11815483
41.7%
bo11295651
39.9%
rt1950447
 
6.9%
ma1504001
 
5.3%
fs1017052
 
3.6%
ba486982
 
1.7%
ie135488
 
0.5%
ap53830
 
0.2%
di33053
 
0.1%
ff31506
 
0.1%
Other values (9)3831
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A13860319
24.5%
L11815483
20.9%
B11782639
20.8%
O11295651
19.9%
R1952888
 
3.4%
T1950447
 
3.4%
M1504005
 
2.7%
F1080102
 
1.9%
S1017085
 
1.8%
I169804
 
0.3%
Other values (5)226225
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)56654648
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A13860319
24.5%
L11815483
20.9%
B11782639
20.8%
O11295651
19.9%
R1952888
 
3.4%
T1950447
 
3.4%
M1504005
 
2.7%
F1080102
 
1.9%
S1017085
 
1.8%
I169804
 
0.3%
Other values (5)226225
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)56654648
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A13860319
24.5%
L11815483
20.9%
B11782639
20.8%
O11295651
19.9%
R1952888
 
3.4%
T1950447
 
3.4%
M1504005
 
2.7%
F1080102
 
1.9%
S1017085
 
1.8%
I169804
 
0.3%
Other values (5)226225
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)56654648
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A13860319
24.5%
L11815483
20.9%
B11782639
20.8%
O11295651
19.9%
R1952888
 
3.4%
T1950447
 
3.4%
M1504005
 
2.7%
F1080102
 
1.9%
S1017085
 
1.8%
I169804
 
0.3%
Other values (5)226225
 
0.4%

canal
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 GiB
COMPRA
23111157 
CONSUMO
3712162 
MAYORISTA
 
1504005

Length

Max length9
Median length6
Mean length6.2903266
Min length6

Characters and Unicode

Total characters178188121
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCOMPRA
2nd rowCOMPRA
3rd rowCOMPRA
4th rowCOMPRA
5th rowCOMPRA

Common Values

ValueCountFrequency (%)
COMPRA23111157
81.6%
CONSUMO3712162
 
13.1%
MAYORISTA1504005
 
5.3%

Length

2025-10-18T15:09:07.375648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-18T15:09:07.408722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
compra23111157
81.6%
consumo3712162
 
13.1%
mayorista1504005
 
5.3%

Most occurring characters

ValueCountFrequency (%)
O32039486
18.0%
M28327324
15.9%
C26823319
15.1%
A26119167
14.7%
R24615162
13.8%
P23111157
13.0%
S5216167
 
2.9%
N3712162
 
2.1%
U3712162
 
2.1%
Y1504005
 
0.8%
Other values (2)3008010
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)178188121
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O32039486
18.0%
M28327324
15.9%
C26823319
15.1%
A26119167
14.7%
R24615162
13.8%
P23111157
13.0%
S5216167
 
2.9%
N3712162
 
2.1%
U3712162
 
2.1%
Y1504005
 
0.8%
Other values (2)3008010
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)178188121
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O32039486
18.0%
M28327324
15.9%
C26823319
15.1%
A26119167
14.7%
R24615162
13.8%
P23111157
13.0%
S5216167
 
2.9%
N3712162
 
2.1%
U3712162
 
2.1%
Y1504005
 
0.8%
Other values (2)3008010
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)178188121
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O32039486
18.0%
M28327324
15.9%
C26823319
15.1%
A26119167
14.7%
R24615162
13.8%
P23111157
13.0%
S5216167
 
2.9%
N3712162
 
2.1%
U3712162
 
2.1%
Y1504005
 
0.8%
Other values (2)3008010
 
1.7%

descr_flag_patente
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 GiB
CON PATENTE
27694136 
SIN PATENTE
 
532478
NaN
 
90113
None
 
10597

Length

Max length11
Median length11
Mean length10.971932
Min length3

Characters and Unicode

Total characters310805481
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCON PATENTE
2nd rowCON PATENTE
3rd rowCON PATENTE
4th rowCON PATENTE
5th rowCON PATENTE

Common Values

ValueCountFrequency (%)
CON PATENTE27694136
97.8%
SIN PATENTE532478
 
1.9%
NaN90113
 
0.3%
None10597
 
< 0.1%

Length

2025-10-18T15:09:07.831920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-18T15:09:07.865539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
patente28226614
49.9%
con27694136
49.0%
sin532478
 
0.9%
nan90113
 
0.2%
none10597
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N56644051
18.2%
T56453228
18.2%
E56453228
18.2%
A28226614
9.1%
P28226614
9.1%
28226614
9.1%
C27694136
8.9%
O27694136
8.9%
S532478
 
0.2%
I532478
 
0.2%
Other values (4)121904
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)310805481
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N56644051
18.2%
T56453228
18.2%
E56453228
18.2%
A28226614
9.1%
P28226614
9.1%
28226614
9.1%
C27694136
8.9%
O27694136
8.9%
S532478
 
0.2%
I532478
 
0.2%
Other values (4)121904
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)310805481
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N56644051
18.2%
T56453228
18.2%
E56453228
18.2%
A28226614
9.1%
P28226614
9.1%
28226614
9.1%
C27694136
8.9%
O27694136
8.9%
S532478
 
0.2%
I532478
 
0.2%
Other values (4)121904
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)310805481
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N56644051
18.2%
T56453228
18.2%
E56453228
18.2%
A28226614
9.1%
P28226614
9.1%
28226614
9.1%
C27694136
8.9%
O27694136
8.9%
S532478
 
0.2%
I532478
 
0.2%
Other values (4)121904
 
< 0.1%

indice_gse
Real number (ℝ)

Missing 

Distinct1357
Distinct (%)< 0.1%
Missing1645935
Missing (%)5.8%
Infinite0
Infinite (%)0.0%
Mean0.1053451
Minimum0
Maximum0.61673068
Zeros102
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size216.1 MiB
2025-10-18T15:09:07.905679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.043121391
Q10.068272055
median0.087306383
Q30.12247283
95-th percentile0.20629466
Maximum0.61673068
Range0.61673068
Interquartile range (IQR)0.054200774

Descriptive statistics

Standard deviation0.066174549
Coefficient of variation (CV)0.62816921
Kurtosis11.497237
Mean0.1053451
Median Absolute Deviation (MAD)0.02393921
Skewness2.8604134
Sum2810753.6
Variance0.0043790709
MonotonicityNot monotonic
2025-10-18T15:09:07.960277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.07329672992133284
 
0.5%
0.08730638337131312
 
0.5%
0.09541343967123953
 
0.4%
0.08526095211110610
 
0.4%
0.06793420572109482
 
0.4%
0.08685738999108945
 
0.4%
0.1896850416108122
 
0.4%
0.07785206482101642
 
0.4%
0.199085232393454
 
0.3%
0.133589150583774
 
0.3%
Other values (1347)25576811
90.3%
(Missing)1645935
 
5.8%
ValueCountFrequency (%)
0102
 
< 0.1%
0.0163337998614316
0.1%
0.0174262847518775
0.1%
0.017623809527120
 
< 0.1%
0.0176507072911037
< 0.1%
0.0184204389618538
0.1%
0.0224066558426988
0.1%
0.0230918727918493
0.1%
0.023371237464248
 
< 0.1%
0.02396529685642
 
< 0.1%
ValueCountFrequency (%)
0.61673067866400
 
< 0.1%
0.5621148523451
 
< 0.1%
0.540464430925800
0.1%
0.533966394719733
0.1%
0.52413369526503
 
< 0.1%
0.51316115954166
 
< 0.1%
0.508160550512256
 
< 0.1%
0.491986847232100
0.1%
0.472669117326456
0.1%
0.44576462123874
 
< 0.1%

n_habitantes
Real number (ℝ)

Missing 

Distinct1312
Distinct (%)< 0.1%
Missing1645935
Missing (%)5.8%
Infinite0
Infinite (%)0.0%
Mean17581.999
Minimum0
Maximum99870
Zeros102
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size216.1 MiB
2025-10-18T15:09:08.013537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1989
Q17382
median14288
Q322370
95-th percentile48215
Maximum99870
Range99870
Interquartile range (IQR)14988

Descriptive statistics

Standard deviation14912.314
Coefficient of variation (CV)0.84815805
Kurtosis6.9860673
Mean17581.999
Median Absolute Deviation (MAD)7179
Skewness2.1597003
Sum4.6911215 × 1011
Variance2.2237711 × 108
MonotonicityNot monotonic
2025-10-18T15:09:08.068865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25363133284
 
0.5%
58080131312
 
0.5%
97044123953
 
0.4%
50776110610
 
0.4%
52411109482
 
0.4%
12776108945
 
0.4%
39175108122
 
0.4%
55031101642
 
0.4%
2095493454
 
0.3%
2491083774
 
0.3%
Other values (1302)25576811
90.3%
(Missing)1645935
 
5.8%
ValueCountFrequency (%)
0102
 
< 0.1%
716
 
< 0.1%
12524
 
< 0.1%
142170
 
< 0.1%
23460
 
< 0.1%
313775
 
< 0.1%
3211177
< 0.1%
342179
 
< 0.1%
469552
< 0.1%
61259
 
< 0.1%
ValueCountFrequency (%)
9987079319
0.3%
97044123953
0.4%
7975514288
 
0.1%
7912471075
0.3%
7395861690
0.2%
7288831521
 
0.1%
6165450881
 
0.2%
6033226456
 
0.1%
6026015662
 
0.1%
58080131312
0.5%

n_ptos_interes
Real number (ℝ)

Missing 

Distinct173
Distinct (%)< 0.1%
Missing476688
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean54.701317
Minimum1
Maximum596
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size216.1 MiB
2025-10-18T15:09:08.122287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q113
median29
Q364
95-th percentile201
Maximum596
Range595
Interquartile range (IQR)51

Descriptive statistics

Standard deviation75.157942
Coefficient of variation (CV)1.3739695
Kurtosis16.749649
Mean54.701317
Median Absolute Deviation (MAD)20
Skewness3.5414581
Sum1.5234665 × 109
Variance5648.7163
MonotonicityNot monotonic
2025-10-18T15:09:08.176309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16678844
 
2.4%
9670499
 
2.4%
5635316
 
2.2%
13580978
 
2.1%
12574507
 
2.0%
6572793
 
2.0%
7572029
 
2.0%
18559950
 
2.0%
2548379
 
1.9%
29544992
 
1.9%
Other values (163)21912349
77.4%
ValueCountFrequency (%)
1495515
1.7%
2548379
1.9%
3484851
1.7%
4498663
1.8%
5635316
2.2%
6572793
2.0%
7572029
2.0%
8540840
1.9%
9670499
2.4%
10362635
1.3%
ValueCountFrequency (%)
59693454
0.3%
53940709
0.1%
50126456
 
0.1%
46183774
0.3%
44648398
0.2%
36534801
 
0.1%
36146739
0.2%
35942367
0.1%
34341592
0.1%
29035781
 
0.1%

superficie_km2
Real number (ℝ)

High correlation  Missing 

Distinct1808
Distinct (%)< 0.1%
Missing476688
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean81.215742
Minimum0.10992095
Maximum17132.919
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size216.1 MiB
2025-10-18T15:09:08.226940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.10992095
5-th percentile0.7644413
Q11.6825796
median8.9982362
Q346.13131
95-th percentile260.90565
Maximum17132.919
Range17132.809
Interquartile range (IQR)44.44873

Descriptive statistics

Standard deviation353.08092
Coefficient of variation (CV)4.3474444
Kurtosis406.73335
Mean81.215742
Median Absolute Deviation (MAD)8.0281871
Skewness14.379017
Sum2.2619101 × 109
Variance124666.14
MonotonicityNot monotonic
2025-10-18T15:09:08.283361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.617478003133284
 
0.5%
24.24593612131312
 
0.5%
22.98189622123953
 
0.4%
26.65355894110610
 
0.4%
62.91280314109482
 
0.4%
142.0838396108945
 
0.4%
11.86972651108122
 
0.4%
64.86280707101642
 
0.4%
1.21264882593454
 
0.3%
117.821319583774
 
0.3%
Other values (1798)26746058
94.4%
(Missing)476688
 
1.7%
ValueCountFrequency (%)
0.109920953319330
0.1%
0.1506689587990
 
< 0.1%
0.22868519191272
 
< 0.1%
0.26628153073149
 
< 0.1%
0.28576828084729
 
< 0.1%
0.30622734988744
 
< 0.1%
0.318493793118681
0.1%
0.321392887210230
< 0.1%
0.324933443561
 
< 0.1%
0.325287553124753
0.1%
ValueCountFrequency (%)
17132.918931476
 
< 0.1%
10403.71132228
 
< 0.1%
9104.6506894
 
< 0.1%
8827.6944919
 
< 0.1%
6473.267074122
 
< 0.1%
5013.033193737
 
< 0.1%
4804.267014269
 
< 0.1%
4753.3807437
 
< 0.1%
4475.3286447606
< 0.1%
4371.35844914677
0.1%

densidad_hab
Real number (ℝ)

High correlation  Missing 

Distinct1357
Distinct (%)< 0.1%
Missing1645935
Missing (%)5.8%
Infinite0
Infinite (%)0.0%
Mean4983.9369
Minimum0
Maximum45213.161
Zeros102
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size216.1 MiB
2025-10-18T15:09:08.340046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16.359227
Q1317.9393
median2767.952
Q38257.1378
95-th percentile15366.71
Maximum45213.161
Range45213.161
Interquartile range (IQR)7939.1985

Descriptive statistics

Standard deviation5959.9686
Coefficient of variation (CV)1.1958355
Kurtosis8.0252512
Mean4983.9369
Median Absolute Deviation (MAD)2682.2504
Skewness2.1004575
Sum1.3297836 × 1011
Variance35521226
MonotonicityNot monotonic
2025-10-18T15:09:08.393286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2943.204496133284
 
0.5%
2395.452983131312
 
0.5%
4222.628066123953
 
0.4%
1905.036401110610
 
0.4%
833.0736732109482
 
0.4%
89.91874119108945
 
0.4%
3300.413026108122
 
0.4%
848.421499101642
 
0.4%
17279.5285693454
 
0.3%
211.421838783774
 
0.3%
Other values (1347)25576811
90.3%
(Missing)1645935
 
5.8%
ValueCountFrequency (%)
0102
 
< 0.1%
0.01553075957460
 
< 0.1%
0.05665253926524
 
< 0.1%
0.058466510319
 
< 0.1%
0.0819880426916
 
< 0.1%
0.18407934192170
 
< 0.1%
0.1891772095196
 
< 0.1%
0.32401108983615
 
< 0.1%
0.39834167512578
< 0.1%
0.54292959129300
< 0.1%
ValueCountFrequency (%)
45213.1607746763
0.2%
43586.4745334988
0.1%
42636.9126528667
0.1%
37937.902434273
0.1%
29464.3281337701
0.1%
25148.0269327046
0.1%
22474.6793418656
 
0.1%
22011.0464918481
 
0.1%
21551.8379515212
 
0.1%
21421.8783427382
0.1%

uf
Real number (ℝ)

High correlation 

Distinct83
Distinct (%)< 0.1%
Missing260451
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean33019.566
Minimum27432.1
Maximum39383.07
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size216.1 MiB
2025-10-18T15:09:08.444487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum27432.1
5-th percentile27565.76
Q128838.63
median33086.83
Q336789.36
95-th percentile39075.41
Maximum39383.07
Range11950.97
Interquartile range (IQR)7950.73

Descriptive statistics

Standard deviation4067.6372
Coefficient of variation (CV)0.12318869
Kurtosis-1.5759363
Mean33019.566
Median Absolute Deviation (MAD)4006.69
Skewness0.061683399
Sum9.2675596 × 1011
Variance16545672
MonotonicityNot monotonic
2025-10-18T15:09:08.498087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35110.98431300
 
1.5%
36789.36410059
 
1.4%
38416.69407005
 
1.4%
35575.48404418
 
1.4%
30991.74394008
 
1.4%
34811.8391564
 
1.4%
30088.37389822
 
1.4%
38647.94388200
 
1.4%
36733.04387052
 
1.4%
31727.74386607
 
1.4%
Other values (73)24076838
85.0%
ValueCountFrequency (%)
27432.1252172
0.9%
27532.8283500
1.0%
27546.22298732
1.1%
27556.9298273
1.1%
27565.76299924
1.1%
27565.79314043
1.1%
27662.17280120
1.0%
27762.55278713
1.0%
27903.3252741
0.9%
27953.42269705
1.0%
ValueCountFrequency (%)
39383.07325489
1.1%
39267.07308601
1.1%
39189.45337662
1.2%
39179.01333226
1.2%
39075.41341065
1.2%
38894.11385217
1.4%
38647.94388200
1.4%
38416.69407005
1.4%
38384.41385482
1.4%
38247.92364850
1.3%

dolar
Real number (ℝ)

High correlation 

Distinct83
Distinct (%)< 0.1%
Missing260451
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean835.72483
Minimum649.92
Maximum992.12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size216.1 MiB
2025-10-18T15:09:08.550741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum649.92
5-th percentile679.86
Q1758.53
median827.84
Q3919.97
95-th percentile980.19
Maximum992.12
Range342.2
Interquartile range (IQR)161.44

Descriptive statistics

Standard deviation95.020588
Coefficient of variation (CV)0.11369841
Kurtosis-1.1303997
Mean835.72483
Median Absolute Deviation (MAD)83.58
Skewness-0.052007123
Sum2.3456183 × 1010
Variance9028.9122
MonotonicityNot monotonic
2025-10-18T15:09:08.602952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
859.51431300
 
1.5%
884.59410059
 
1.4%
992.12407005
 
1.4%
789.32404418
 
1.4%
850.25394008
 
1.4%
905.7391564
 
1.4%
803.59389822
 
1.4%
951.21388200
 
1.4%
932.66387052
 
1.4%
787.16386607
 
1.4%
Other values (73)24076838
85.0%
ValueCountFrequency (%)
649.92298273
1.1%
666.76298732
1.1%
669.43283500
1.0%
677.67280120
1.0%
679.86252741
0.9%
681.09299924
1.1%
693.31252172
0.9%
695.69314043
1.1%
699.98269705
1.0%
705.09295538
1.0%
ValueCountFrequency (%)
992.12407005
1.4%
988.1385482
1.4%
982.38374233
1.3%
980.19381528
1.3%
978.07333226
1.2%
977.32364850
1.3%
967.48325489
1.1%
966377581
1.3%
956.58353874
1.2%
951.21388200
1.4%

ipc
Real number (ℝ)

Zeros 

Distinct20
Distinct (%)< 0.1%
Missing260451
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean0.46172049
Minimum-0.5
Maximum1.9
Zeros1684246
Zeros (%)5.9%
Negative3255225
Negative (%)11.5%
Memory size216.1 MiB
2025-10-18T15:09:08.648017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.5
5-th percentile-0.2
Q10.1
median0.4
Q30.7
95-th percentile1.3
Maximum1.9
Range2.4
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.46158654
Coefficient of variation (CV)0.99970989
Kurtosis0.17339507
Mean0.46172049
Median Absolute Deviation (MAD)0.3
Skewness0.56905211
Sum12959050
Variance0.21306213
MonotonicityNot monotonic
2025-10-18T15:09:08.689635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0.33058689
10.8%
0.43057898
10.8%
0.12938499
10.4%
0.72126496
 
7.5%
0.21939548
 
6.8%
0.51787039
 
6.3%
-0.11782117
 
6.3%
01684246
 
5.9%
0.81451072
 
5.1%
1.21445668
 
5.1%
Other values (10)6795601
24.0%
ValueCountFrequency (%)
-0.5410059
 
1.4%
-0.4308601
 
1.1%
-0.2754448
 
2.7%
-0.11782117
6.3%
01684246
5.9%
0.12938499
10.4%
0.21939548
6.8%
0.33058689
10.8%
0.43057898
10.8%
0.51787039
6.3%
ValueCountFrequency (%)
1.9386607
 
1.4%
1.4685585
 
2.4%
1.3382388
 
1.3%
1.21445668
5.1%
1.1789900
 
2.8%
1739060
 
2.6%
0.91045239
3.7%
0.81451072
5.1%
0.72126496
7.5%
0.61293714
4.6%

imacec
Real number (ℝ)

Zeros 

Distinct63
Distinct (%)< 0.1%
Missing260451
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean2.5527125
Minimum-15.3
Maximum20.1
Zeros1098185
Zeros (%)3.9%
Negative8317156
Negative (%)29.4%
Memory size216.1 MiB
2025-10-18T15:09:08.736262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-15.3
5-th percentile-5.3
Q1-0.5
median1.8
Q33.8
95-th percentile18.1
Maximum20.1
Range35.4
Interquartile range (IQR)4.3

Descriptive statistics

Standard deviation6.3254893
Coefficient of variation (CV)2.4779482
Kurtosis1.7177419
Mean2.5527125
Median Absolute Deviation (MAD)2.2
Skewness0.61982505
Sum71646658
Variance40.011815
MonotonicityNot monotonic
2025-10-18T15:09:08.791962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-11188802
 
4.2%
2.51113599
 
3.9%
01098185
 
3.9%
2.3949812
 
3.4%
-0.4749989
 
2.6%
18.1707561
 
2.5%
0.3705930
 
2.5%
-1.2689336
 
2.4%
1.8688218
 
2.4%
6.4675477
 
2.4%
Other values (53)19499964
68.8%
ValueCountFrequency (%)
-15.3228088
0.8%
-14.1220231
0.8%
-12.4201830
0.7%
-11.3287858
1.0%
-10.7233166
0.8%
-5.3305032
1.1%
-3.5270761
1.0%
-3.4291382
1.0%
-3.3318903
1.1%
-3.1323870
1.1%
ValueCountFrequency (%)
20.1364001
1.3%
19.1381116
1.3%
18.1707561
2.5%
15.6389822
1.4%
15382388
1.3%
14.3380931
1.3%
14.1295538
1.0%
10.1394008
1.4%
9349381
1.2%
7.2386607
1.4%

tpm
Real number (ℝ)

High correlation 

Distinct26
Distinct (%)< 0.1%
Missing260451
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean5.1719741
Minimum0.5
Maximum11.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size216.1 MiB
2025-10-18T15:09:08.841166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.5
Q11.75
median5
Q38.25
95-th percentile11.25
Maximum11.25
Range10.75
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation3.6801485
Coefficient of variation (CV)0.71155586
Kurtosis-1.1947318
Mean5.1719741
Median Absolute Deviation (MAD)3.25
Skewness0.31851947
Sum1.4516114 × 108
Variance13.543493
MonotonicityNot monotonic
2025-10-18T15:09:08.884497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.54483323
15.8%
11.253428216
 
12.1%
52553232
 
9.0%
2.751613034
 
5.7%
1.751597455
 
5.6%
31455762
 
5.1%
8.251142986
 
4.0%
5.51064923
 
3.8%
91053915
 
3.7%
5.75992068
 
3.5%
Other values (16)8681959
30.6%
ValueCountFrequency (%)
0.54483323
15.8%
0.75764602
 
2.7%
1270761
 
1.0%
1.5389822
 
1.4%
1.751597455
 
5.6%
2304012
 
1.1%
2.5818737
 
2.9%
2.751613034
 
5.7%
31455762
 
5.1%
4394008
 
1.4%
ValueCountFrequency (%)
11.253428216
12.1%
10.75377581
 
1.3%
10.25740610
 
2.6%
9.75705219
 
2.5%
9.5380498
 
1.3%
91053915
 
3.7%
8.251142986
 
4.0%
7.25755761
 
2.7%
7727563
 
2.6%
6.5357805
 
1.3%

tasa_desempleo
Real number (ℝ)

Missing 

Distinct70
Distinct (%)< 0.1%
Missing601516
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean8.5004554
Minimum6.7
Maximum13.09
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size216.1 MiB
2025-10-18T15:09:08.931787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6.7
5-th percentile6.85
Q17.8
median8.41
Q38.89
95-th percentile11.21
Maximum13.09
Range6.39
Interquartile range (IQR)1.09

Descriptive statistics

Standard deviation1.294189
Coefficient of variation (CV)0.15224937
Kurtosis2.4211403
Mean8.5004554
Median Absolute Deviation (MAD)0.52
Skewness1.3980505
Sum2.3568199 × 108
Variance1.6749251
MonotonicityNot monotonic
2025-10-18T15:09:08.983674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.08789393
 
2.8%
8.04759777
 
2.7%
7.8732482
 
2.6%
8.68728107
 
2.6%
8.52709790
 
2.5%
7.19690299
 
2.4%
8.89673056
 
2.4%
7.81652022
 
2.3%
6.7612316
 
2.2%
9605849
 
2.1%
Other values (60)20772717
73.3%
(Missing)601516
 
2.1%
ValueCountFrequency (%)
6.7612316
2.2%
6.8582232
2.1%
6.85299924
1.1%
6.86280120
1.0%
6.88318903
1.1%
6.98341139
1.2%
7.01291382
1.0%
7.05304012
1.1%
7.09278713
1.0%
7.1504913
1.8%
ValueCountFrequency (%)
13.09233166
0.8%
12.93287858
1.0%
12.35305032
1.1%
12.25201830
0.7%
11.58326174
1.2%
11.21228088
0.8%
10.76341475
1.2%
10.35329602
1.2%
10.3329975
1.2%
10.29372408
1.3%

Interactions

2025-10-18T15:07:13.337142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:53:45.534826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:54:34.624716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:55:23.284532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:56:11.778907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:57:02.974146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:57:58.596990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:58:52.354306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:59:43.057048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:00:34.728297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:01:27.729632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:02:19.849858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:03:07.156661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:03:55.883223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:04:45.477221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:05:34.951793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:06:24.249408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:07:16.336707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:53:48.275956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:54:37.281027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:55:25.998751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:56:15.003040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:57:06.038673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:58:01.468295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:58:55.276513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:59:46.180955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:00:37.823790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:01:30.692840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:02:22.633488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:03:10.114404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:03:58.860862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:04:48.444487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:05:37.901988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:06:27.232279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:07:19.261907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:53:51.045877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:54:40.063850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:55:28.797221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:56:17.856260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:57:09.101614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:58:04.632312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:58:58.312006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:59:49.574782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:00:41.195072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:01:33.770337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:02:25.473134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:03:13.066383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:04:01.862174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:04:51.380827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:05:40.895346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:06:30.370975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:07:22.150283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:53:53.808658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:54:42.814168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:55:31.603384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:56:20.521127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:57:12.266617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:58:07.674111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:59:01.438530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:59:52.779080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:00:44.654141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:01:36.928708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:02:28.218101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:03:15.994969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:04:04.824107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:04:54.292745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:05:43.844697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:06:33.306966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:07:25.222258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:53:56.786576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:54:45.775144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:55:34.536442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:56:23.420299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:57:15.479985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:58:10.647401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:59:04.383712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:59:55.859564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:00:47.738880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:01:40.071638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:02:30.953099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:03:18.919936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:04:07.900608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:04:57.212292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:05:46.812207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:06:36.221477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:07:28.205929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:53:59.554923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:54:48.524883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:55:37.272672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:56:26.280217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:57:18.693310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:58:13.551571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:59:07.420010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:59:58.812974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:00:51.187779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:01:43.066556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:02:33.695075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:03:21.834924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:04:10.991470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:05:00.185171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:05:49.750898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:06:39.141348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:07:30.941159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:54:02.380574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:54:51.321497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:55:40.071413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:56:29.021431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:57:21.654367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-10-18T14:59:10.766894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-10-18T15:01:46.195279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:02:36.455422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:03:24.591972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:04:13.874897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:05:02.985476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:05:52.517129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:06:41.922161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:07:33.674784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:54:05.218601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:54:54.136267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:55:42.877759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:56:31.916063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:57:24.652307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:58:19.683488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:59:13.565480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:00:04.672204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:00:57.671065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:01:49.605131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:02:39.209464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:03:27.367875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:04:16.687495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:05:05.749149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:05:55.275192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:06:44.674752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:07:36.529742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:54:08.179205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:54:57.071141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:55:45.760820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:56:35.095846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:57:27.811594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:58:22.734934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:59:16.405947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:00:07.611088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-10-18T15:02:41.982283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:03:30.213026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-10-18T15:05:58.148577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:06:47.529850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:07:39.379098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:54:11.114828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:55:00.018975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:55:48.666055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:56:37.967100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:57:30.894070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:58:25.735751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:59:19.361538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:00:10.598079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:01:03.757449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:01:55.720898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:02:44.749710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:03:33.078810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:04:22.492336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:05:11.597143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-10-18T15:06:50.406353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:07:42.131019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:54:13.938545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:55:02.932936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:55:51.443111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:56:40.771605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:57:33.876720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:58:28.560958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:59:22.137434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:00:13.566376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:01:06.704118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:01:58.924593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:02:47.384456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:03:35.854841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:04:25.269698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:05:14.578122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:06:03.770736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:06:53.158679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:07:44.992110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:54:16.903779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:55:05.927336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:55:54.353079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:56:44.018215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:57:37.136720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:58:31.557312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:59:24.923938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:00:16.587579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:01:09.718683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:02:02.029642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:02:50.156584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:03:38.606816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:04:28.177514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:05:17.622424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:06:06.676912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:06:56.022905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:07:47.844646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:54:19.845584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:55:08.930599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:55:57.258088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:56:47.299630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:57:40.579010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:58:34.687480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:59:27.849254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:00:19.579379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:01:12.782858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:02:04.948654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:02:52.901221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:03:41.509632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:04:30.978495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:05:20.517164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:06:09.544983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:06:58.889653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:07:50.710553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:54:22.890607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:55:11.880134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:56:00.152856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:56:50.397686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:57:43.779526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:58:37.886835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:59:30.729171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:00:22.584666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:01:15.821812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:02:07.825516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:02:55.653277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:03:44.408170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:04:33.878946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:05:23.301475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:06:12.423469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:07:01.773549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:07:53.559636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:54:26.033679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:55:14.802261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:56:03.060414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:56:53.571056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:57:47.032731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:58:41.774085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:59:34.004106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:00:25.542092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:01:18.931658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:02:10.740217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:02:58.414391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:03:47.267218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:04:36.768287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:05:26.192543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:06:15.239952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:07:04.680333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:07:56.401845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:54:28.968960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:55:17.700196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:56:05.967556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:56:56.831300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:57:51.231724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:58:46.197343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:59:36.808223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:00:28.501094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:01:21.897957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:02:13.644535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:03:01.159918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:03:50.137960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:04:39.658820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:05:29.102592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:06:18.214273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:07:07.452827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:07:59.126904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:54:31.860864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:55:20.554911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:56:08.870344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:56:59.841925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:57:55.613951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:58:49.310926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T14:59:39.993266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:00:31.537668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:01:24.774257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:02:17.022722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:03:04.057723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:03:52.946982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:04:42.528502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:05:31.961359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:06:21.249024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T15:07:10.340894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-18T15:09:09.037280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
canaldensidad_habdescr_flag_patentedolarid_barrioid_clienteid_comunaid_periodoid_sku_ventaimacecindice_gseipcliq_umn_habitantesn_ptos_interessegmentosuperficie_km2tasa_desempleotipo_mixtpmuf
canal1.0000.0400.1230.0650.0640.0350.0640.0820.1520.0520.1760.0210.0380.0950.1851.0000.0310.0880.0870.0860.094
densidad_hab0.0401.0000.015-0.0220.276-0.0730.275-0.0250.0230.0000.400-0.0030.0270.3820.0840.123-0.907-0.0140.022-0.010-0.024
descr_flag_patente0.1230.0151.0000.0420.0420.0270.0590.0490.0290.0250.0400.0160.0000.0170.0280.3050.0180.0430.0040.0440.048
dolar0.065-0.0220.0421.000-0.005-0.004-0.0050.8640.247-0.032-0.0040.169-0.0670.0050.0000.0340.0290.1410.1320.5180.865
id_barrio0.0640.2760.042-0.0051.000-0.1110.997-0.0060.031-0.0010.0580.0050.0300.0860.1220.170-0.202-0.0000.063-0.002-0.006
id_cliente0.035-0.0730.027-0.004-0.1111.000-0.110-0.007-0.017-0.002-0.042-0.013-0.003-0.024-0.0240.0580.057-0.0060.054-0.012-0.006
id_comuna0.0640.2750.059-0.0050.997-0.1101.000-0.0060.032-0.0010.0580.0050.0300.0850.1210.170-0.202-0.0000.063-0.002-0.006
id_periodo0.082-0.0250.0490.864-0.006-0.007-0.0061.0000.2930.019-0.0060.086-0.0800.008-0.0010.0540.0330.3310.1710.5791.000
id_sku_venta0.1520.0230.0290.2470.031-0.0170.0320.2931.0000.013-0.0120.034-0.1150.050-0.0300.121-0.0010.1150.1990.1590.290
imacec0.0520.0000.025-0.032-0.001-0.002-0.0010.0190.0131.0000.0050.2510.011-0.0030.0070.039-0.002-0.2470.071-0.1690.019
indice_gse0.1760.4000.040-0.0040.058-0.0420.058-0.006-0.0120.0051.000-0.002-0.0300.2740.4150.120-0.371-0.0230.1200.007-0.005
ipc0.021-0.0030.0160.1690.005-0.0130.0050.0860.0340.251-0.0021.0000.013-0.0000.0020.0180.004-0.1150.0720.2130.077
liq_um0.0380.0270.000-0.0670.030-0.0030.030-0.080-0.1150.011-0.0300.0131.0000.023-0.0260.018-0.018-0.0100.010-0.070-0.080
n_habitantes0.0950.3820.0170.0050.086-0.0240.0850.0080.050-0.0030.274-0.0000.0231.0000.2630.091-0.0350.0180.027-0.0020.008
n_ptos_interes0.1850.0840.0280.0000.122-0.0240.121-0.001-0.0300.0070.4150.002-0.0260.2631.0000.100-0.076-0.0260.0840.012-0.001
segmento1.0000.1230.3050.0340.1700.0580.1700.0540.1210.0390.1200.0180.0180.0910.1001.0000.0220.0450.1280.0450.053
superficie_km20.031-0.9070.0180.029-0.2020.057-0.2020.033-0.001-0.002-0.3710.004-0.018-0.035-0.0760.0221.0000.0250.0180.0100.032
tasa_desempleo0.088-0.0140.0430.141-0.000-0.006-0.0000.3310.115-0.247-0.023-0.115-0.0100.018-0.0260.0450.0251.0000.144-0.1840.331
tipo_mix0.0870.0220.0040.1320.0630.0540.0630.1710.1990.0710.1200.0720.0100.0270.0840.1280.0180.1441.0000.1530.166
tpm0.086-0.0100.0440.518-0.002-0.012-0.0020.5790.159-0.1690.0070.213-0.070-0.0020.0120.0450.010-0.1840.1531.0000.579
uf0.094-0.0240.0480.865-0.006-0.006-0.0061.0000.2900.019-0.0050.077-0.0800.008-0.0010.0530.0320.3310.1660.5791.000

Missing values

2025-10-18T15:08:01.977445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-18T15:08:18.981729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-10-18T15:08:50.825385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

id_categoriaid_clienteid_periodotipo_mixid_sku_ventaliq_umid_barrioid_comunasegmentocanaldescr_flag_patenteindice_gsen_habitantesn_ptos_interessuperficie_km2densidad_habufdolaripcimacectpmtasa_desempleo
01693134202110PREMIUM4504034.32432430115.04301.0BOCOMPRACON PATENTE0.07781931953.083.07.7952044099.05862230380.53805.471.315.02.758.08
11693134202110PREMIUM4504171.17600430115.04301.0BOCOMPRACON PATENTE0.07781931953.083.07.7952044099.05862230380.53805.471.315.02.758.08
21693134202110PREMIUM4506847.26432430115.04301.0BOCOMPRACON PATENTE0.07781931953.083.07.7952044099.05862230380.53805.471.315.02.758.08
31693134202110PREMIUM4507040.22176430115.04301.0BOCOMPRACON PATENTE0.07781931953.083.07.7952044099.05862230380.53805.471.315.02.758.08
41693134202110PREMIUM4507463.47424430115.04301.0BOCOMPRACON PATENTE0.07781931953.083.07.7952044099.05862230380.53805.471.315.02.758.08
51693134202110PREMIUM4507490.67200430115.04301.0BOCOMPRACON PATENTE0.07781931953.083.07.7952044099.05862230380.53805.471.315.02.758.08
61693134202110PREMIUM4508500.55272430115.04301.0BOCOMPRACON PATENTE0.07781931953.083.07.7952044099.05862230380.53805.471.315.02.758.08
71693134202110PREMIUM4508511.57920430115.04301.0BOCOMPRACON PATENTE0.07781931953.083.07.7952044099.05862230380.53805.471.315.02.758.08
81693134202110PREMIUM4508590.33264430115.04301.0BOCOMPRACON PATENTE0.07781931953.083.07.7952044099.05862230380.53805.471.315.02.758.08
91693134202111MASIVO4501330.23688430115.04301.0BOCOMPRACON PATENTE0.07781931953.083.07.7952044099.05862230762.80836.730.514.32.757.53
id_categoriaid_clienteid_periodotipo_mixid_sku_ventaliq_umid_barrioid_comunasegmentocanaldescr_flag_patenteindice_gsen_habitantesn_ptos_interessuperficie_km2densidad_habufdolaripcimacectpmtasa_desempleo
283273141658679202508PREMIUM74930.05544811215.08112.0RTCONSUMOCON PATENTE0.08615724304.076.035.636473681.99790539383.07967.480.00.54.758.56
283273151658679202508PREMIUM4502950.05544811215.08112.0RTCONSUMOCON PATENTE0.08615724304.076.035.636473681.99790539383.07967.480.00.54.758.56
283273161658679202508PREMIUM4503020.05544811215.08112.0RTCONSUMOCON PATENTE0.08615724304.076.035.636473681.99790539383.07967.480.00.54.758.56
283273171658679202508PREMIUM4503360.05964811215.08112.0RTCONSUMOCON PATENTE0.08615724304.076.035.636473681.99790539383.07967.480.00.54.758.56
283273181658679202508PREMIUM4504030.05544811215.08112.0RTCONSUMOCON PATENTE0.08615724304.076.035.636473681.99790539383.07967.480.00.54.758.56
283273191658679202508PREMIUM4507040.05544811215.08112.0RTCONSUMOCON PATENTE0.08615724304.076.035.636473681.99790539383.07967.480.00.54.758.56
283273201658679202508PREMIUM6045810.05544811215.08112.0RTCONSUMOCON PATENTE0.08615724304.076.035.636473681.99790539383.07967.480.00.54.758.56
283273211658553202508PREMIUM4503020.05544NaN8101.0IECONSUMONaNNaNNaNNaNNaNNaN39383.07967.480.00.54.758.56
283273221658553202508PREMIUM4505510.21000NaN8101.0IECONSUMONaNNaNNaNNaNNaNNaN39383.07967.480.00.54.758.56
283273231658553202508PREMIUM4513710.21000NaN8101.0IECONSUMONaNNaNNaNNaNNaNNaN39383.07967.480.00.54.758.56